• Journal of Korea Game Society
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• v.21 no.4
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• pp.73-84
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• 2021

In this paper, we propose an analysis method that categorizes the Core that essential behaviors in game, the Primary that solves the game problem, and the Secondary that helps the Core and the Primary. The proposed method can analyze the genre similarity and characteristics of the game, the richness of the content, and the proficiency level of the game. case study were conducted to confirm whether the analysis items were consistent with the objective game experience. The results of this study are expected to be helpful in improving game design ability.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.30-30
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• 2000

In the three axis control of satellite by using reaction wheel and gyro, a Gyro carries out measuring of the attitude angie and the attitude angular velocity. The Gyro is operated by the electronic part and the mechanic actuator. The digital part of the electronic part is consisted of the FPGA (Field Programmable Gate Array), which is one of the methods for designing VLSI (Very Large Scale Integrated Circuit), and the mechanic actuator processes the input/output data by the dynamic model. In the research of the mixed mode of Gyro, the simulation is accomplished by SABER of the mixed mode simulator and the results for the practical implementation of the satellite ACS (Attitude Control System) interfaced with the data processing are proposed.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1641-1644
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• 2005

Magnetic actuation utilizes the mechanic torque that is the result of interaction of the current in a coil with an external magnetic field. A main obstacle is, however, that torques can only be produced perpendicular to the magnetic field. In addition, there is uncertainty in the Earth magnetic field models due to the complicated dynamic nature of the field. Also, the magnetic hardware and the spacecraft can interact, causing both to behave in undesirable ways. This actuation principle has been a topic of research since earliest satellites were launched. Earlier magnetic control has been applied for nutation damping for gravity gradient stabilized satellites, and for velocity decrease for satellites without appendages. The three axes of a micro-satellite can be stabilized by using an electromagnetic actuator which is rigidly mounted on the structure of the satellite. The actuator consists of three mutually-orthogonal air-cored coils on the skin of the satellite. The coils are excited so that the orbital frame magnetic field and body frame magnetic field coincides i.e. to make the Euler angles to zero. This can be done using a Neural Network controller trained by PD controller data and driven by the difference between the orbital and body frame magnetic fields.

• Ocean Systems Engineering
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• v.14 no.2
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• pp.101-114
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• 2024

Seafloor sediment mapping is an essential research topic in shallow coastal waters, especially in port development, benthic habitat mapping, and underwater communications. The seafloor sediments can be interpreted by collecting sediment samples directly in the field using a grab sampler or corer. Another method is optical, especially using underwater cameras and videos. Both methods each have weaknesses in terms of area coverage (mechanic) and accurate positioning (optic). The latest technology used to overcome it is the acoustic method (echosounder) with Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) positioning. Therefore, in this study will propose the classification of seafloor sediments in coastal waters using acoustic method that is Multibeam Echosounder (MBES) multi-frequency with five frequency (200 kHz, 250 kHz, 300 kHz, 350 kHz, and 400 kHz). In this study, the deep neural network (DNN) used the bathymetric multi frequency, bathymetric difference inters frequencies, and bathymetric features from 5 (five) frequencies as input layer and 4 (four) sediment types in 74 (seventy-four) sample sediment as output layer to make a seafloor sediment map. Results of sediment mapping using the DNN method show an overall accuracy of 71.6% (significant) and a kappa coefficient of 0.59 (moderate). The distribution of seafloor sediment in the study area is mainly silt (41.6%), followed by clayey sand (36.6%), sandy silt (14.2%), and silty sand (7.5%).